Hasil untuk "Economic growth, development, planning"

M. Lindner, Michael Maroschek, S. Netherer et al.

A. Hall, W. Janssen, E. Pehu et al.

Agricultural development depends to a great extent on how successfully knowledge is generated and applied. Investments in knowledge - especially in the form of science and technology have featured prominently and consistently in most strategies to promote sustainable and equitable agricultural development at the national level. As the context of agricultural development has changed, ideas of what constitutes innovation have changed, and so have approaches for investing in it. Strengthened research systems may increase the supply of new knowledge and new technologies, but they may not necessarily improve the capacity for innovation throughout the agricultural sector. The concept of an innovation system has guided this more holistic approach to planning knowledge production and use. This paper uses this concept to develop a framework for guiding diagnosis of innovation capacity and for planning interventions. The innovation systems concept focuses not merely on the science suppliers but on the totality and interaction of actors involved in innovation. It extends beyond the creation of knowledge to encompass the factors affecting demand for and use of new and existing knowledge in novel and useful ways. The question then is whether the principles and insights arising from the innovation systems concept and the perspective on innovation capacity development it implies can be converted into operational tools for policies and projects that address the practical challenges of agricultural development and sustained economic growth. This paper attempts to answer that question. Chapter one presents why assess the value of the innovation systems perspective? The innovation systems concept is discussed in chapter two, especially with regard to its potential value for agricultural development interventions. Chapter three describes the methodology for the study, further discusses the rationale for selecting each case study, and summarizes results of each study. Chapter four, a comparative analysis of the eight studies highlights differences in the evolution of the eight cases and identifies potential sources of these differences. The main findings from the case studies are used in chapter five to derive lessons on what drives innovation and the generic interventions that promote the capacity to innovate. The comparative analysis of the case studies is used to develop an intervention framework in chapter six. Chapter seven recapitulates the main conclusions from the case studies, revisits the utility of the analytical framework for understanding agricultural innovation, and also revisits the value of the intervention framework for identifying activities in support of agricultural innovation.

De Zhou, Jianchun Xu, Zhulu Lin

Canlei Liu, Ruixue Jiang, Li Jiang

Exploiting the quasi-natural experiment of the U.S.-China trade friction, we empirically examine the impact of uncertainty shocks on Chinese firms' overseas patent applications and transnational innovation collaboration. We find that uncertainty shocks suppress Chinese firms’ overseas innovation performance, resulting in a disruption effect on innovation collaboration with the U.S., but a diversion effect on collaborations with other countries, which is particularly evident in U.S. innovation partners, Europe, Association of South East Asian Nations (ASEAN), Japan, and South Korea. Heterogeneity analysis shows that these shocks disproportionately affect high-tech industries, industries with strong influence, and those with close U.S.-China innovation linkages.

Alessandro Bellina, Paolo Buttà, Vito D. P. Servedio

Economic complexity algorithms aim to uncover the hidden capabilities that drive economic systems. Here, we present a fundamental reinterpretation of two of these algorithms, the Economic Complexity Index (ECI) and the Economic Fitness and Complexity (EFC), by reformulating them as optimization problems that minimize specific cost functions. We show that ECI computation is equivalent to finding eigenvectors of the network's transition matrix by minimizing the quadratic form associated with the network's Laplacian. For EFC, we derive a novel cost function that exploits the algorithm's intrinsic logarithmic structure and clarifies the role of the regularization parameter in its non-homogeneous version. Additionally, we establish the existence and uniqueness of its solution, providing theoretical foundations for its application. This optimization-based reformulation bridges economic complexity and established frameworks in spectral theory, network science, and optimization. The theoretical insights translate into practical computational advantages: we introduce a conservative, gradient-based update rule that substantially accelerates algorithmic convergence, with potential implications for a broader class of algorithms, including the Sinkhorn-Knopp method. Finally, we apply the energetic framework to a real-world trade network, demonstrating how link-wise energy provides a direct way to identify structurally relevant and vulnerable regions of the export matrix, thus complementing and enriching standard economic complexity analyses. Beyond advancing our theoretical understanding of economic complexity indicators, this work opens new pathways for algorithmic improvements and extends applicability to general network structures beyond traditional bipartite economic networks.

Silvia Montagnani, Barnabe Ledoux, David Lacoste

Despite decades of climate policy and rapid improvements in energy efficiency, global CO2 emissions continue to rise, suggesting the presence of structural drivers that offset efficiency gains. Here we identify financial leverage as a key mechanism underpinning this persistent overshoot. We develop a stochastic macro-financial model linking credit dynamics, economic growth, bankruptcy risk, and cumulative carbon emissions. The model shows that debt-financed growth systematically amplifies cumulative emissions, locking economies into high-carbon trajectories even as emissions intensity declines. This arises from a double constraint: debt repayment requires sustained growth, while growth remains energy-dependent and thus generates emissions. When growth becomes increasingly dependent on leverage, financial instability and cumulative emissions rise, while gains in real wealth diminish, revealing a leverage frontier beyond which additional credit primarily generates risk. Calibrating the model to multi-decade data for the US, China, France and Denmark, we find a robust coupling between debt accumulation, cumulative GDP and cumulative emissions across distinct economic structures. These results indicate that achieving net-zero targets requires aligning credit allocation with decarbonisation objectives

Bogdan Oancea

The term of big data was used since 1990s, but it became very popular around 2012. A recent definition of this term says that big data are information assets characterized by high volume, velocity, variety and veracity that need special analytical methods and software technologies to extract value form them. While big data was used at the beginning mostly in information technology field, now it can be found in every area of activity: in governmental decision-making processes, manufacturing, education, healthcare, economics, engineering, natural sciences, sociology. The rise of Internet, mobile phones, social media networks, different types of sensors or satellites provide enormous quantities of data that can have profound effects on economic research. The data revolution that we are facing transformed the way we measure the human behavior and economic activities. Unemployment, consumer price index, population mobility, financial transactions are only few examples of economic phenomena that can be analyzed using big data sources. In this paper we will start with a taxonomy of big data sources and show how these new data sources can be used in empirical analyses and to build economic indicators very fast and with reduced costs.

Sergei Masaev

The activity of a special economic zone is defined by a dynamic equation, taking into account the individual strategies of residents. At a given point in time, in respect to the resident enterprise of a special economic zone, a regime is introduced that limits the flow of resources by 80% (sanctions), forming an integral indicator for a comprehensive assessment of the impact of sanctions on the enterprise. On the basis of the dynamic equation, an estimate of the economic damage for the potential SEZ of the Krasnoyarsk Territory is given.

Yue Chen, Mohan Li

When analyzing the components influencing the stock prices, it is commonly believed that economic activities play an important role. More specifically, asset prices are more sensitive to the systematic economic news that impose a pervasive effect on the whole market. Moreover, the investors will not be rewarded for bearing idiosyncratic risks as such risks are diversifiable. In the paper Economic Forces and the Stock Market 1986, the authors introduced an attribution model to identify the specific systematic economic forces influencing the market. They first defined and examined five classic factors from previous research papers: Industrial Production, Unanticipated Inflation, Change in Expected Inflation, Risk Premia, and The Term Structure. By adding in new factors, the Market Indices, Consumptions and Oil Prices, one by one, they examined the significant contribution of each factor to the stock return. The paper concluded that the stock returns are exposed to the systematic economic news, and they are priced with respect to their risk exposure. Also, the significant factors can be identified by simply adopting their model. Driven by such motivation, we conduct an attribution analysis based on the general framework of their model to further prove the importance of the economic factors and identify the specific identity of significant factors.

Idris A. Adediran, Raymond Swaray, Aminat O. Orekoya et al.

Purpose – This study aims to examine the ability of clean energy stocks to provide cover for investors against market risks related to climate change and disturbances in the oil market. Design/methodology/approach – The study adopts the feasible quasi generalized least squares technique to estimate a predictive model based on Westerlund and Narayan’s (2015) approach to evaluating the hedging effectiveness of clean energy stocks. The out-of-sample forecast evaluations of the oil risk-based and climate risk-based clean energy predictive models are explored using Clark and West’s model (2007) and a modified Diebold & Mariano forecast evaluation test for nested and non-nested models, respectively. Findings – The study finds ample evidence that clean energy stocks may hedge against oil market risks. This result is robust to alternative measures of oil risk and holds when applied to data from the COVID-19 pandemic. In contrast, the hedging effectiveness of clean energy against climate risks is limited to 4 of the 6 clean energy indices and restricted to climate risk measured with climate policy uncertainty. Originality/value – The study contributes to the literature by providing extensive analysis of hedging effectiveness of several clean energy indices (global, the United States (US), Europe and Asia) and sectoral clean energy indices (solar and wind) against oil market and climate risks using various measures of oil risk (WTI (West Texas intermediate) and Brent volatility) and climate risk (climate policy uncertainty and energy and environmental regulation) as predictors. It also conducts forecast evaluations of the clean energy predictive models for nested and non-nested models.

Ammar Jamshed

Quantum computing is an advancing area of computing sciences and provides a new base of development for many futuristic technologies discussions on how it can help developing economies will further help developed economies in technology transfer and economic development initiatives related to Research and development within developing countries thus providing a new means of foreign direct investment(FDI) and business innovation for the majority of the globe that lacks infrastructure economic resources required for growth in the technology landscape and cyberinfrastructure for growth in computing applications. Discussion of which areas of support quantum computing can help will further assist developing economies in implementing it for growth opportunities for local systems and businesses.

Shiro Armstrong, Danny Quah

This paper casts within a unified economic framework some key challenges for the global economic order: de-globalization; the rising impracticability of global cooperation; and the increasingly confrontational nature of Great Power competition. In these, economics has been weaponised in the service of national interest. This need be no bad thing. History provides examples where greater openness and freer trade emerge from nations seeking only to advance their own self-interests. But the cases described in the paper provide mixed signals. We find that some developments do draw on a growing zero-sum perception to economic and political engagement. That zero-sum explanation alone, however, is crucially inadequate. Self-serving nations, even when believing the world zero-sum, have under certain circumstances produced outcomes that have benefited all. In other circumstances, perfectly-predicted losses have instead resulted on all sides. Such lose-lose outcomes -- epic fail equilibria -- generalize the Prisoner's Dilemma game and are strictly worse than zero-sum. In our analysis, Third Nations -- those not frontline in Great Power rivalry -- can serve an essential role in averting epic fail outcomes. The policy implication is that Third Nations need to provide platforms that will gently and unobtrusively nudge Great Powers away from epic-fail equilibria and towards inadvertent cooperation.

Aziz ur Rehman, Ahsan Tanveer, M. Touseef Ashraf et al.

Autonomous ground vehicle systems have found extensive potential and practical applications in the modern world. The development of an autonomous ground vehicle poses a significant challenge, particularly in identifying the best path plan, based on defined performance metrics such as safety margin, shortest time, and energy consumption. Various techniques for motion planning have been proposed by researchers, one of which is the use of artificial potential fields. Several authors in the past two decades have proposed various modified versions of the artificial potential field algorithms. The variations of the traditional APF approach have given an answer to prior shortcomings. This gives potential rise to a strategic survey on the improved versions of this algorithm. This study presents a review of motion planning for autonomous ground vehicles using artificial potential fields. Each article is evaluated based on criteria that involve the environment type, which may be either static or dynamic, the evaluation scenario, which may be real-time or simulated, and the method used for improving the search performance of the algorithm. All the customized designs of planning models are analyzed and evaluated. At the end, the results of the review are discussed, and future works are proposed.

Yipeng Xu, He Sun, Junfeng Zhu

The Maasai Mara in Kenya, renowned for its biodiversity, is witnessing ecosystem degradation and species endangerment due to intensified human activities. Addressing this, we introduce a dynamic system harmonizing ecological and human priorities. Our agent-based model replicates the Maasai Mara savanna ecosystem, incorporating 71 animal species, 10 human classifications, and 2 natural resource types. The model employs the metabolic rate-mass relationship for animal energy dynamics, logistic curves for animal growth, individual interactions for food web simulation, and human intervention impacts. Algorithms like fitness proportional selection and particle swarm mimic organism preferences for resources. To guide preservation activities, we formulated 21 management strategies encompassing tourism, transportation, taxation, environmental conservation, research, diplomacy, and poaching, employing a game-theoretic framework. Using the TOPSIS method, we prioritized four key developmental indicators: environmental health, research advancement, economic growth, and security. The interplay of 16 factors determines these indicators, each influenced by our policies to varying degrees. By evaluating the policies' repercussions, we aim to mitigate adverse animal-human interactions and equitably address human concerns. We classified the policy impacts into three categories: Environmental Preservation, Economic Prosperity, and Holistic Development. By applying these policy groupings to our ecosystem model, we tracked the effects on the intricate animal-human-resource dynamics. Utilizing the entropy weight method, we assessed the efficacy of these policy clusters over a decade, identifying the optimal blend emphasizing both environmental conservation and economic progression.

Bilal Ahmed Memon

Purpose ― The global pandemic COVID-19 has attracted considerable interest from researchers globally. However, there is very little systematic work on the impact of the COVID-19 crisis on the local stock markets. This paper proposes a complex network method that examines the effects of global pandemic COVID-19 on the Pakistan stock market to fill in these gaps. Methods ― Firstly, correlograms are plotted to inspect the correlation matrices of the overall and two sub-sample periods. Secondly, correlation threshold networks and topological properties are examined for different threshold levels. Finally, this paper uses evolving MSTs to construct a dynamical complex network and presents dynamic centrality measures, normalised tree, and average path lengths. Findings ― The findings show that COVID-19 related certainty and crisis lead to low volatility and a star-like structure, resulting in a quick flow of information and a strong correlation among the Pakistan stock market. Implication ― This analysis would help investors and regulators to manage the Pakistan stock market better. In addition, the comprehensive study solely on the Pakistan stock market will be helpful for Pakistan government officials and stock market participants to assess and predict the risks of the Pakistan stock market associated with the global pandemic COVID-19.  Originality ― This paper addresses both classes of the networks. To the best of our knowledge, the static and dynamic evolution of the Pakistan stock market around the global pandemic COVID-19 has not been performed yet.

Walton Stinson

Economic Sanctions are being deployed by the West in the Russia-Ukraine conflict at a level never before attempted with the intention of wrecking the Russian economy. Very little attention is given to the dismal record of sanctions or the consequences of sanctions on civilians, mercantile enterprises, global and regional economies, and the economies of countries applying the sanctions. This paper examines the unintended consequences of sanctions and argues that they are ineffective at countering military aggression because they are ambiguous when calibrated against military actions which require precise responses with strong signals. Criteria for the evaluation of possible responses to military aggression are proposed.

Kabiru Hannafi Ibrahim, Dyah Wulan Sari, Rossanto Dwi Handoyo

To mitigate carbon emissions studies have incorporated trade and energy as determinants of emissions in the environmental Kuznets curve model. These studies mostly focused on the overall trade without regard to goods trade that is more polluting. To this end, this study used a panel of African countries and investigate the role of goods trade and energy in generating carbon emissions. We utilized random coefficients and the generalised method of moment. Our findings confirmed the existence of the environmental Kuznets curve hypothesis. Findings further indicate that trade increases emissions and there exists evidence of non-linear nexus between trade and emissions. The composition effect increases emissions but the effect is not robust to different estimates. Energy increases emissions, and the indirect effect of trade through energy revealed no evidence that trade has allowed Africa the use of an energy-efficient technique of production which reduces carbon emissions. Findings also confirmed the existence of income and factor abundance pollution haven hypothesis. Therefore, trade and energy should be considered in emissions mitigation policy.

Fabian Krüger, Hendrik Plett

The fixed-event forecasting setup is common in economic policy. It involves a sequence of forecasts of the same (`fixed') predictand, so that the difficulty of the forecasting problem decreases over time. Fixed-event point forecasts are typically published without a quantitative measure of uncertainty. To construct such a measure, we consider forecast postprocessing techniques tailored to the fixed-event case. We develop regression methods that impose constraints motivated by the problem at hand, and use these methods to construct prediction intervals for gross domestic product (GDP) growth in Germany and the US.

Svitlana Tkalenko, Tetyana Melnyk, Liudmyla Kudyrko

The main goal of the study is to identify endogenous and exogenous factors that determine the scale and dynamics of Ukraine’s exports of organic agricultural food products (OAP). The formulated goal caused assessment of a number of potential factors influencing the development of the export potential of the Ukrainian agro-industrial complex in terms of production and sales of organic agricultural products on foreign markets. The authors conducted economic and mathematical modeling based on the software product E-Views. The observation interval covers 2008-2019. Multifactorial regression model has been constructed and tested for heteroscedasticity, as well as causal relationships have been identified between the main indicators of supply and demand and the exports volumes of related organic products. This makes it possible for further forecast on Ukraine’s exports in the short and medium term. Methodology. The study has been based on statistics from international and Ukrainian institutions specializing in organic farming and trade, including FiBL (Research Institute of Organic Agriculture), the Federation of Organic Movement of Ukraine. Databases of the State Statistics Service of Ukraine, UNCTAD for the period 2008-2019 were also involved, which made it possible to conduct a full cycle of research procedures in order to identify the most significant factors influencing Ukraine’s export activity within related segment of the global market. The results of the conducted modeling show the following: achievement and increase of Ukraine’s relative advantage in international trade of organic agro-food products for the outlined years; the existence of strong connection between the volume of exports of organic agro-food products and the level of comparative country’s advantages in international trade; identifying a significant impact on exports of endogenous factors, namely the level of wholesale and retail sales on the domestic market of Ukraine as a factor that creates additional demand from the population and business of Ukraine (B2B and B2C markets) for organic products and enhances the attention of agricultural manufacturers to activities that combine the criteria of high profitability and public demand. Another endogenous factor is the volume of areas allocated for organic farming has shown insignificant impact, however, it allows to create resource conditions for increasing production and export activity of national business in a particular sphere on various directions, from meat and dairy products to production of organic fruit, vegetables, etc. Practical implications. Conceptual provisions, conclusions formulated by the authors based on the conducted econometric modeling, allow to optimize the measures of regulatory policy in terms of institutional support of conditions and factors contributing to promising activities of the national agro-industrial complex. This will ensure the implementation of the national strategy on sustainable development with its emphasis achieving environmental criteria of production and consumption, reduce the level of import dependence upon a number of strategically important food groups and, at the same time, increase economic efficiency of Ukrainian agricultural business. Value/originality. Prospects for further research in this area may assess the potential of international production and marketing cooperation between Ukrainian agricultural companies and non-resident companies in terms of limiting the latter’s access to the land market in Ukraine while finding flexible mechanisms to stimulate joint production and sale of organic agricultural products on international markets according to quality and safety standards.

Thierry Grenet, Rafik Ballou, Quentin Basto et al.

In this note we report on the development plans and first results of the Grenoble Axion Haloscope (GrAHal) project. It is aimed at developing a haloscope platform dedicated to the search for axion dark matter particles. We discuss its general framework and the plans to reach the sensitivity required to probe well known invisible axion models, over particularly relevant axion masses and coupling regions. We also present our first haloscope prototype and the result of its test run at liquid He temperature, setting a new exclusion limit $g_{a γγ} \leq 2.2 \times 10^{-13}~ \text{GeV}^{-1}$ ($g_{a γγ} \leq 22 \times g_{\text{KSVZ}}$) around 6.375 GHz ($m_a \simeq 26.37$ $μ\text{eV}$).